Influence Propagation in Large, Blog Graphs
Web-logs (‘blogs’) have created a social phenomenon, often influencing political opinions, and often creating market or opinion trends. Web-logs cite each other, creating a rich network that we propose to mine for patterns: Which is the most influential blog? How does the blog graph look like? How does it evolve over time? These are the questions we want to address, the ultimate goal being to forecast which trends will prevail.

In our previous work, we were able to classify shapes of information cascades and identify common influence patterns in a recommendation network. Additionally, we have found power law relationships among graph nodes in a citation network as well as a network in the blog domain. Here we propose the following steps: (a) discovery of static patterns, that is, patterns in a snapshot of a blog graph (b) temporal patterns, to study how blog citations appear over time (in bursts? Uniformly? Exponential explosion, followed by exponential decay?) (c) classification and forecasting: given a snapshot of the blog graph, can we spot the most influential nodes? Can we forecast whether they will continue being influential?

We expect our findings to have even broader applicability, for example in virus propagation patterns and in fashion/marketing demand patterns.